Visualization & Biology: Challenges and Perspectives

Thursday, September 19, 08:45 - 18:00
VilVite Center, Bergen

The rapid growth in volume, complexity, and diversity of biological data represents an increasing challenge for researchers in many areas. The aim of this workshop is to bring together experts from biology, bioinformatics, and visualization to develop a joint understanding of the key technologies, obstacles, and opportunities involved in in generating insight from these large and highly complex data sets.

Target Audience

The target audience covers, analogously to the spirit of the event, a wide spectrum of disciplines. This includes researchers from visualization, bioinformatics, and biology, as well as illustrators and others interested in automated techniques for meaningful knowledge discovery in and communication of complex biological data and processes.

Workshop Program

The workshop program will consist of interleaved presentations by international and local invited speakers from multiple disciplines. Additionally, the program will feature an invited talk by Ewan Birney which is part of the Horizons lecture series.

Session 1: 08:45 - 10:30

Metabolic modelling: From networks to dynamics and back

Metabolic modelling has gained considerable interest in different biological fields. Although having mostly been used to simulate metabolic changes in microbes, it has now more and more been applied to simulate metabolic changes in higher eukaryotes, too. Metabolic modelling has therefore applications ranging from biotechnology, to improve industrial production of desirable compounds, to pharmacology, to predict potential drug targets.
I will give an overview of modelling techniques used in metabolic modelling to date including their applications and limits. I will furthermore point to current problems and challenges in (metabolic) modelling like scalability and the implementation of regulatory events.

Approaches for Visualizing Human Physiology

Crossover Between Individual and Quantity Based Simulation for Interactive Molecular Illustration

Computational biology develops structural and procedural descriptions of life machinery.Current visualization technologies convey either form or function, but not how these two aspects interact. In this talk we present a work in progress of visualization techniques that communicates how molecular processes work, inspired by the craft of scientific animators.To allow such an interactive and scientific animation we bring together kinetic model of the processes and the structure of participating elements. This results in a crossover system, based on both quantitative and agent based modelling, which expresses in an illustrative and interactive way the results of the quantitative modelling.

Session 2: 10:45 - 12:15

Visual analytics in omics: Why, what and how?

As biological datasets become bigger and more complex, their analysis gets ever more difficult and automated algorithms increasingly act as black boxes. In addition, the entire paradigm of biological research is changing: where we used to always start from a well-defined hypothesis and then generate experimental data to (dis)prove that hypothesis, we now often have large datasets to our disposal and need to explore these in order to generate new hypotheses. In this talk, I will cover the topic of visual analytics, an analysis approach that gets increasing attention as a possible part of a solution. What is it? How can it help? How do we build our tools?

Animating Biology: The making of a science film

Biology is complicated. Understanding cellular and molecular biology is particularly difficult. Amongst the many effective communication tools at our disposal, animated film is at the forefront. Animation brings biological stories to life, and thus aids in our understanding of biological structure and function. Whether used to inform patients, to teach undergraduate biology, or to disseminate advances in research, animated film is a tool worth employing. But what goes into an animation? I will explain the step-by-step process of creating animations and show a lot of inspiring examples.

Session 3: 13:15- 14:45

Digital brain atlasing: 3-D "Google maps" of the brain

Brain research is carried out using a large range of techniques and advanced equipment, resulting in enormous quantities of data. Recent technological developments in the area of digital atlasing are currently influencing how this data deluge is being managed and analysed. Traditional brain atlases contain maps, typically diagrams of two-dimensional section planes through the brain, as well as accompanying relevant information. Such atlases are useful references and fill the needs of many neuroscientists. But given the need to integrate vast amount of heterogeneous data, they offer several limitations. Digital brain atlases are more dynamic and powerful, aiming at tying together and sharing multimodal data and information. Multiple R&D projects around the world are currently developing digital brain atlases as frameworks to link heterogeneous data from different sources and related information. Digital brain atlases, building on the same principles as Google maps or Google Earth, but developed to handle considerably more complex information, can provide a critically important positioning service for brain data. The coordinated efforts of many laboratories and enterprises in the field of digital brain atlasing represent the basis for future large scale integration of information into a Virtual Brain. A critical mass of data will allow realistic modeling of brain function, likely to have a massive impact on both the health and technology sectors.

High-dimensional data (hundreds of dimensions, or more) and temporal data (thousands of time frames) pose substantial challenges for both computational and interactive analysis. To reveal relevant intrinsic relations between items or dimensions, the utilization of only computational methods or standard visualization techniques is not enough. In this talk, we introduce the concept of interactive visual analysis (IVA) that enables us to combine computational methods with the user knowledge through a system of multiple linked views on the data and advanced interaction mechanisms. Our approach allows us to interact with the data on the level of individual items and also on the level of dimensions, exploiting a number of useful statistical methods in addition. To improve the understanding of temporal data, we utilize clustering methods, where the user is provided means to understand the internal cluster structure. Moreover, we also showcase how IVA can be beneficial when analyzing molecular dynamics.

Horizon Lecture: 15:15- 16:30

Understanding basic biology using outbred genetics

There is increasing ability to collect phenotypic data in the context of genotype data. This phenotype data ranges from molecular data sets at the cellular level, such as RNA expression, chromatin accessibility, transcription factor binding, to whole animal phenotypes, such as morphometric measurements, imaging based measurements and disease status. These phenotypes can be collected in a variety of different species - most obviously human but also laboratory species (such as flies or fish) where there are available population panels. In my talk I will outline the research in exploring the correlations between these phenotypes and genotypes. Correlative approaches are particularly powerful when genotype data is introduced, as the vast majority of genotypes do not change over the lifetime of individuals; thus any association to genotypes must have the causality in only one direction. I will illustrate this research in a human study of the transcription factor CTCF in lymphoblastoid cell lines. During this study we serendipitously discovered a radical difference in CTCF behaviour during X-inactivation, highlighting the role of this transcription factor in chromatin structure. Finally I will outline the strategic role of EBI and Elixir databases, drawing from this research experience, and the importance of an information infrastructure in the context of life science.